Érick Oliveira Rodrigues

Isaac Fiuza Vieira, Kathya Silvia Collazos Linares, Esteban Walter Gonzalez Clua et al.

Procedural Content Generation (PCG) has become an essential technique in game development due to its ability to reduce production time and cost while increasing replayability and variety. However, when not aligned with level design principles, PCG can lead to incoherent spatial structures and poor gameplay experiences. Objective: This work proposes a PCG method guided by level design principles to generate structured indoor environments - such as houses, mansions, and dungeons - aiming to ensure both architectural coherence and navigability. Methodology: The method is divided into three main stages: segmentation of the space using Binary Space Partitioning (BSP); logical connection of rooms based on graph traversal to prevent redundant links; and a post-processing stage responsible for cleaning structural artifacts and improving visual cohesion. The methodology allows parameterization of room area and shape, with randomness controlled via seeds for reproducibility. Results: Two experiments were conducted. The first demonstrated the flexibility of the methodology under different seeds and parameter configurations. The second evaluated the navigability of generated maps by verifying connectivity using Breadth-First Search (BFS). In this test, 100,000 maps were generated, and with suitable parameters, over 91% of them achieved complete connectivity.

Érick Oliveira Rodrigues, Felipe Fernandes Cordeiro de Morais, Aura Conci

The quantification of fat depots on the surroundings of the heart is an accurate procedure for evaluating health risk factors correlated with several diseases. However, this type of evaluation is not widely employed in clinical practice due to the required human workload. This work proposes a novel technique for the automatic segmentation of cardiac fat pads. The technique is based on applying classification algorithms to the segmentation of cardiac CT images. Furthermore, we extensively evaluate the performance of several algorithms on this task and discuss which provided better predictive models. Experimental results have shown that the mean accuracy for the classification of epicardial and mediastinal fats has been 98.4% with a mean true positive rate of 96.2%. On average, the Dice similarity index, regarding the segmented patients and the ground truth, was equal to 96.8%. Therfore, our technique has achieved the most accurate results for the automatic segmentation of cardiac fats, to date.

Eduardo Luiz Alba, Gilson Adamczuk Oliveira, Matheus Henrique Dal Molin Ribeiro et al.

Electricity expense management presents significant challenges, as this resource is susceptible to various influencing factors. In universities, the demand for this resource is rapidly growing with institutional expansion and has a significant environmental impact. In this study, the machine learning models long short-term memory (LSTM), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost) were trained with historical consumption data from the Federal Institute of Paraná (IFPR) over the last seven years and climatic variables to forecast electricity consumption 12 months ahead. Datasets from two campuses were adopted. To improve model performance, feature selection was performed using Shapley additive explanations (SHAP), and hyperparameter optimization was carried out using genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate that the proposed cooperative ensemble learning approach named Weaker Separator Booster (WSB) exhibited the best performance for datasets. Specifically, it achieved an sMAPE of 13.90% and MAE of 1990.87 kWh for the IFPR-Palmas Campus and an sMAPE of 18.72% and MAE of 465.02 kWh for the Coronel Vivida Campus. The SHAP analysis revealed distinct feature importance patterns across the two IFPR campuses. A commonality that emerged was the strong influence of lagged time-series values and a minimal influence of climatic variables.

João Henrique Pereira Machado, Gilson Adamczuk Oliveira, Érick Oliveira Rodrigues

In this work, we utilize image segmentation to visually identify blood vessels in retinal examination images. This process is typically carried out manually. However, we can employ heuristic methods and machine learning to automate or at least expedite the process. In this context, we propose a cross-platform, open-source, and responsive software that allows users to manually segment a retinal image. The purpose is to use the user-segmented image to retrain machine learning algorithms, thereby enhancing future automated segmentation results. Moreover, the software also incorporates and applies certain image filters established in the literature to improve vessel visualization. We propose the first solution of this kind in the literature. This is the inaugural integrated software that embodies the aforementioned attributes: open-source, responsive, and cross-platform. It offers a comprehensive solution encompassing manual vessel segmentation, as well as the automated execution of classification algorithms to refine predictive models.

Érick Oliveira Rodrigues, Esteban Clua

This work proposes an automatic real time lighting technique for procedurally generated isometric maps. The scenario is generated from a string seed and the proposed lighting system estimates the geometrical shape of the 2D objects as if they were 3D for further light interaction, therefore producing a 2.5D effect. We employ opacity maps to overcome an issue generated by the geometrical shape estimation. The solution is a coupled approach between the CPU and GPU. The produced visuals, gameplay and performance were evaluated by gamers, programmers and designers. Furthermore, the performance, in terms of frames per second, was evaluated over distinct graphics cards and processors and was satisfactory.

Érick Oliveira Rodrigues, FFC Morais, NAOS Morais et al.

The deposits of fat on the surroundings of the heart are correlated to several health risk factors such as atherosclerosis, carotid stiffness, coronary artery calcification, atrial fibrillation and many others. These deposits vary unrelated to obesity, which reinforces its direct segmentation for further quantification. However, manual segmentation of these fats has not been widely deployed in clinical practice due to the required human workload and consequential high cost of physicians and technicians. In this work, we propose a unified method for an autonomous segmentation and quantification of two types of cardiac fats. The segmented fats are termed epicardial and mediastinal, and stand apart from each other by the pericardium. Much effort was devoted to achieve minimal user intervention. The proposed methodology mainly comprises registration and classification algorithms to perform the desired segmentation. We compare the performance of several classification algorithms on this task, including neural networks, probabilistic models and decision tree algorithms. Experimental results of the proposed methodology have shown that the mean accuracy regarding both epicardial and mediastinal fats is 98.5% (99.5% if the features are normalized), with a mean true positive rate of 98.0%. In average, the Dice similarity index was equal to 97.6%.

Érick Oliveira Rodrigues

This work proposes a distance that combines Minkowski and Chebyshev distances and can be seen as an intermediary distance. This combination not only achieves efficient run times in neighbourhood iteration tasks in Z^2, but also obtains good accuracies when coupled with the k-Nearest Neighbours (k-NN) classifier. The proposed distance is approximately 1.3 times faster than Manhattan distance and 329.5 times faster than Euclidean distance in discrete neighbourhood iterations. An accuracy analysis of the k-NN classifier using a total of 33 datasets from the UCI repository, 15 distances and values assigned to k that vary from 1 to 200 is presented. In this experiment, the proposed distance obtained accuracies that were better than the average more often than its counterparts (in 26 cases out of 33), and also obtained the best accuracy more frequently (in 9 out of 33 cases).

Muriel Mazzetto, Marcelo Teixeira, Érick Oliveira Rodrigues et al.

Automotive manufacturing assembly tasks are built upon visual inspections such as scratch identification on machined surfaces, part identification and selection, etc, which guarantee product and process quality. These tasks can be related to more than one type of vehicle that is produced within the same manufacturing line. Visual inspection was essentially human-led but has recently been supplemented by the artificial perception provided by computer vision systems (CVSs). Despite their relevance, the accuracy of CVSs varies accordingly to environmental settings such as lighting, enclosure and quality of image acquisition. These issues entail costly solutions and override part of the benefits introduced by computer vision systems, mainly when it interferes with the operating cycle time of the factory. In this sense, this paper proposes the use of deep learning-based methodologies to assist in visual inspection tasks while leaving very little footprints in the manufacturing environment and exploring it as an end-to-end tool to ease CVSs setup. The proposed approach is illustrated by four proofs of concept in a real automotive assembly line based on models for object detection, semantic segmentation, and anomaly detection.